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Found 17 papers, 11 papers with code
Target Really Matters: Target-aware Contrastive Learning and Consistency Regularization for Few-shot Stance Detection
Despite the significant progress on this task, it is extremely time-consuming and budget-unfriendly to collect sufficient high-quality labeled data for every new target under fully-supervised learning, whereas unlabeled data can be collected easier.
Past, Present, and Future: Conversational Emotion Recognition through Structural Modeling of Psychological Knowledge
Furthermore, we utilize CSK to enrich edges with knowledge representations and process the SKAIG with a graph transformer.
Ranked #7 on
Emotion Recognition in Conversation
on EmoryNLP
Question-Interlocutor Scope Realized Graph Modeling over Key Utterances for Dialogue Reading Comprehension
Although these tasks are effective, there are still urging problems: (1) randomly masking speakers regardless of the question cannot map the speaker mentioned in the question to the corresponding speaker in the dialogue, and ignores the speaker-centric nature of utterances.
Compressing And Debiasing Vision-Language Pre-Trained Models for Visual Question Answering
To facilitate the application of VLP to VQA tasks, it is imperative to jointly study VLP compression and OOD robustness, which, however, has not yet been explored.
A Win-win Deal: Towards Sparse and Robust Pre-trained Language Models
In response to the efficiency problem, recent studies show that dense PLMs can be replaced with sparse subnetworks without hurting the performance.
Towards Robust Visual Question Answering: Making the Most of Biased Samples via Contrastive Learning
However, these models reveal a trade-off that the improvements on OOD data severely sacrifice the performance on the in-distribution (ID) data (which is dominated by the biased samples).
Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQA
To overcome this limitation, we propose a new dataset that considers varying types of shortcuts by constructing different distribution shifts in multiple OOD test sets.
Neutral Utterances are Also Causes: Enhancing Conversational Causal Emotion Entailment with Social Commonsense Knowledge
Conversational Causal Emotion Entailment aims to detect causal utterances for a non-neutral targeted utterance from a conversation.
Ranked #1 on
Causal Emotion Entailment
on RECCON
Learning to Win Lottery Tickets in BERT Transfer via Task-agnostic Mask Training
Firstly, we discover that the success of magnitude pruning can be attributed to the preserved pre-training performance, which correlates with the downstream transferability.
Check It Again:Progressive Visual Question Answering via Visual Entailment
Besides, they only explore the interaction between image and question, ignoring the semantics of candidate answers.
Check It Again: Progressive Visual Question Answering via Visual Entailment
Besides, they only explore the interaction between image and question, ignoring the semantics of candidate answers.
A Hierarchical Transformer with Speaker Modeling for Emotion Recognition in Conversation
It can be regarded as a personalized and interactive emotion recognition task, which is supposed to consider not only the semantic information of text but also the influences from speakers.
Ranked #21 on
Emotion Recognition in Conversation
on IEMOCAP
SPOC learner's final grade prediction based on a novel sampling batch normalization embedded neural network method
To solve this problem, a sampling batch normalization embedded deep neural network (SBNEDNN) method is developed in this paper.
Learning Class-Transductive Intent Representations for Zero-shot Intent Detection
A critical problem behind these limitations is that the representations of unseen intents cannot be learned in the training stage.
Modeling Intra and Inter-modality Incongruity for Multi-Modal Sarcasm Detection
Inspired by this, we propose a BERT architecture-based model, which concentrates on both intra and inter-modality incongruity for multi-modal sarcasm detection.
Hyperspectral Image Classification Method Based on 2D–3D CNN and Multibranch Feature Fusion
Then, by means of the multibranch neural network, three kinds of features from shallow to deep are extracted and fused in the spectral dimension.
A lateral semicircular canal segmentation based geometric calibration for human temporal bone CT Image
Computed Tomography (CT) of the temporal bone has become an important method for diagnosing ear diseases.
